Abstract: Aspects of the technology include methods and systems for performing remote adjustments to a ventilator with a remote device. A remote device may include an interactive display including a remote position indicator. The remote position indicator may be associated or correlated with a local ventilator position indicator. A selection and/or adjustment at the remote device (or an activation at the remote device) at the interactive display may result in a selection, adjustment, or activation at the ventilator. Information may be transmitted to the ventilator from the remote device to remotely adjust the ventilator. Additionally or alternatively, the remote device may additionally display a view of, or replicate, some or all portions of the ventilator display.

Abstract: Aspects of the technology include methods and systems for performing remote adjustments to a ventilator with a remote device. A remote device may include an interactive display including a remote position indicator. The remote position indicator may be associated or correlated with a local ventilator position indicator. A selection and/or adjustment at the remote device (or an activation at the remote device) at the interactive display may result in a selection, adjustment, or activation at the ventilator. Information may be transmitted to the ventilator from the remote device to remotely adjust the ventilator. Additionally or alternatively, the remote device may additionally display a view of, or replicate, some or all portions of the ventilator display.

Abstract: Aspects of the technology include methods and systems for performing remote adjustments to a ventilator with a remote device. A remote device may include an interactive display including a remote position indicator. The remote position indicator may be associated or correlated with a local ventilator position indicator. A selection and/or adjustment at the remote device (or an activation at the remote device) at the interactive display may result in a selection, adjustment, or activation at the ventilator. Information may be transmitted to the ventilator from the remote device to remotely adjust the ventilator. Additionally or alternatively, the remote device may additionally display a view of, or replicate, some or all portions of the ventilator display.

Abstract: Aspects of the technology include methods and systems for performing remote adjustments to a ventilator with a remote device. A remote device may include an interactive display including a remote position indicator. The remote position indicator may be associated or correlated with a local ventilator position indicator. A selection and/or adjustment at the remote device (or an activation at the remote device) at the interactive display may result in a selection, adjustment, or activation at the ventilator. Information may be transmitted to the ventilator from the remote device to remotely adjust the ventilator. Additionally or alternatively, the remote device may additionally display a view of, or replicate, some or all portions of the ventilator display.

Abstract: A mechanism for compiling a generative description of an inference task into a neural network. First, an arbitrary generative probabilistic model from the exponential family is specified (or received). The model characterizes a conditional probability distribution for measurement data given a set of latent variables. A factor graph is generated for the generative probabilistic model. Each factor node of the factor graph is expanded into a corresponding sequence of arithmetic operations, based on a specified inference task and a kind of message passing algorithm. The factor graph and the sequences of arithmetic operations specify the structure of a neural network for performance of the inference task. A learning algorithm is executed, to determine values of parameters of the neural network. The neural network is then ready for performing inference on operational measurements.

Abstract: A mechanism for compiling a generative description of an inference task into a neural network. First, an arbitrary generative probabilistic model from the exponential family is specified (or received). The model characterizes a conditional probability distribution for measurement data given a set of latent variables. A factor graph is generated for the generative probabilistic model. Each factor node of the factor graph is expanded into a corresponding sequence of arithmetic operations, based on a specified inference task and a kind of message passing algorithm. The factor graph and the sequences of arithmetic operations specify the structure of a neural network for performance of the inference task. A learning algorithm is executed, to determine values of parameters of the neural network. The neural network is then ready for performing inference on operational measurements.